{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gumbel-Softmax - New feature\n",
    "This notebook showcases a new feature introduced in version 0.6, Gumbel-Softmax activations!\n",
    "\n",
    "**Structure of the notebook:**\n",
    "\n",
    "1. A quick recap on categorical feature synthesis\n",
    "2. Softmax and the Gumbel-Softmax activation\n",
    "3. Synthesized categorical features comparison\n",
    "    * Raw sample format comparison\n",
    "    * Synthesized samples comparison (categoricals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A quick recap on categorical feature synthesis\n",
    "Before synthesizing we typically preprocess our features. In the case of categorical features, one-hot encodings are frequently used in order to transform discrete features into sparse blocks of 1's and 0's. Converting symbolic inputs like categorical features to sparse arrays allows neural network (NN) models to handle the data similarly to very different feature formats like numerical continuous features.\n",
    "\n",
    "An example:\n",
    "* Before one-hot encoding:\n",
    "\n",
    "<style>\n",
    "th {\n",
    "  padding-top: 5px;\n",
    "  padding-right: 10px;\n",
    "  padding-bottom: 5px;\n",
    "  padding-left: 10px;\n",
    "}\n",
    "</style>\n",
    "\n",
    "| ID | Gender | AgeRange |\n",
    "| :------------: | :-------:  | :-------:  |\n",
    "| 1 | Male | 20-29 |\n",
    "| 2 | Female | 10-19 |\n",
    "\n",
    "* After one-hot encoding:\n",
    "\n",
    "| ID | Gender_Male | Gender_Female | AgeRange_10-19 | AgeRange_20-29 |\n",
    "| :------------: | :-------:  | :-------:  | :-------:  | :-------:  |\n",
    "| 1 | 1 | 0 | 0 | 1 |\n",
    "| 2 | 0 | 1 | 1 | 0 |\n",
    "\n",
    "GANs attempt to synthesize these sparse distributions as they appear on real data. However, despite the input categorical features having a sparse format, NN classifiers learn __[logits](https://en.wikipedia.org/wiki/Logit)__, non-normalized probability distributions, for each class represented in the one-hot encoded input. Without activation layers that can handle this output, you might get synthetic records looking something like this:\n",
    "\n",
    "| ID | Gender_Male | Gender_Female | AgeRange_10-19 | AgeRange_20-29 |\n",
    "| :------------: | :-------:  | :-------:  | :-------:  | :-------:  |\n",
    "| 1 | 0.867 | 0.622 | -0.155 | 0.855 |\n",
    "| 2 | 0.032 | 1.045 | 0.901 | -0.122 |\n",
    "\n",
    "This looks messy; leaves you with the job of inferring a sensible output (p.e. use the class with highest activation) and also is a potential flag for a GAN discriminator to identify fake samples.\n",
    "\n",
    "Let's see what Gumbel-Softmax is and what it can do about to fix the issue!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Softmax and the Gumbel-Softmax activation\n",
    "Softmax is a differentiable family of functions that map an array of logits to probabilities, i.e. values are bounded in the range $[0, 1]$ and sum to 1.\n",
    "These are often used for turning logits into probability distributions from which we can sample. However these samples can't help us in gradient descent model learning because they are obtained from a random process (no relation with the model's parameters).\n",
    "\n",
    "The Gumbel-Softmax (GS) is a special kind of Softmax function that got introduced in 2016 (fun fact: coincidentally it was proposed in the same time by two independent teams) __[\\[1](https://arxiv.org/abs/1611.00712)__, __[2\\]](https://arxiv.org/abs/1611.01144)__. It works like a continuous approximation of Softmax. Instead of using logits directly __[Gumbel distribution](https://en.wikipedia.org/wiki/Gumbel_distribution)__ noise is added before the softmax operation so that our model is outputting a combination from a deterministic component, parameterized by the mean and the variance of the categorical distribution, and a stochastic component, the Gumbel noise, which is just helping us sample without adding bias to the process.\n",
    "\n",
    "A temperature parameter, usually called tau or lambda and defined in $]0, inf[$ is used to tune this distribution between the true categorical distribution and a uniform distribution respectively. This parameter is usually kept close to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthesized categorical features comparison\n",
    "Now we are moving to a comparison of results before/after GS activation was added.\n",
    "\n",
    "We will do this by first looking at raw samples format (using samples as they leave the generator, before inverting any pre-processing) and synthetic samples categorical distributions with histograms.\n",
    "For this comparisons we will leverage the WGAN with Gradient Penalty implementation of the library on the adult dataset. The available snippets should reproduce the results but the takeaways are fully delivered on the cached results of this notebook.\n",
    "Since the new feature is already implemented in our WGAN with Gradient Penalty implementation, we will inherit it and make a very simple override so that we use a generator without the GS activation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raw sample format comparison\n",
    "This comparison is similar to the examples in the introduction section. We are looking for one-hot encoded features as the samples leave the generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmlb import fetch_data\n",
    "\n",
    "from ydata_synthetic.synthesizers.regular.wgangp.model import WGAN_GP\n",
    "from ydata_synthetic.synthesizers import ModelParameters, TrainParameters\n",
    "\n",
    "data = fetch_data('adult')\n",
    "num_cols = ['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
    "cat_cols = ['workclass','education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',\n",
    "            'native-country', 'target']\n",
    "\n",
    "\n",
    "# WGAN_GP training\n",
    "# Defining the training parameters of WGAN_GP\n",
    "\n",
    "noise_dim = 128\n",
    "dim = 128\n",
    "batch_size = 50\n",
    "\n",
    "log_step = 100\n",
    "epochs = 50\n",
    "learning_rate = [5e-4, 3e-3]\n",
    "beta_1 = 0.5\n",
    "beta_2 = 0.9\n",
    "\n",
    "gan_args = ModelParameters(batch_size=batch_size,\n",
    "                           lr=learning_rate,\n",
    "                           betas=(beta_1, beta_2),\n",
    "                           noise_dim=noise_dim,\n",
    "                           layers_dim=dim)\n",
    "\n",
    "train_args = TrainParameters(epochs=epochs,\n",
    "                             sample_interval=log_step)\n",
    "\n",
    "n_discriminator = 3\n",
    "sample_size = 15000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mimicking the WGAN_GP implementation without GS\n",
    "class NoGS_WGAN_GP(WGAN_GP):\n",
    "    \"\"\"The simple override of the define_gan below blocks the generator from plugging in the GS activation layer.\n",
    "    This makes it equivalent to the previous implementation.\n",
    "    The source code will help you understanding how it works\"\"\"\n",
    "    def define_gan(self, activation_info = None):\n",
    "        optimizers = super().define_gan(activation_info=None)\n",
    "        return optimizers"
   ]
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   "cell_type": "code",
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      "/home/aquemy/project/ydata/dev/ydata/.venv/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "WGAN_GP without GS version train\n"
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      "/home/aquemy/project/ydata/dev/ydata/.venv/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
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      "Epoch: 37 | disc_loss: 0.039680153131484985 | gen_loss: 0.6376960277557373\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|█████████████████████████████████▌         | 39/50 [11:48<03:27, 18.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 38 | disc_loss: -0.09816781431436539 | gen_loss: 0.9127862453460693\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|██████████████████████████████████▍        | 40/50 [12:06<03:06, 18.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 39 | disc_loss: -0.12906982004642487 | gen_loss: 0.9056374430656433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|███████████████████████████████████▎       | 41/50 [12:24<02:46, 18.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40 | disc_loss: -0.0923251211643219 | gen_loss: 1.0344390869140625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 84%|████████████████████████████████████       | 42/50 [12:43<02:27, 18.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 41 | disc_loss: -0.026309426873922348 | gen_loss: 1.040420651435852\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████████████████████████████████▉      | 43/50 [13:01<02:08, 18.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 42 | disc_loss: -0.0011439509689807892 | gen_loss: 0.836100161075592\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|█████████████████████████████████████▊     | 44/50 [13:19<01:50, 18.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 43 | disc_loss: -0.06541667878627777 | gen_loss: 0.4965837597846985\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|██████████████████████████████████████▋    | 45/50 [13:38<01:32, 18.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 44 | disc_loss: nan | gen_loss: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|███████████████████████████████████████▌   | 46/50 [13:57<01:14, 18.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 45 | disc_loss: nan | gen_loss: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|████████████████████████████████████████▍  | 47/50 [14:16<00:55, 18.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 46 | disc_loss: nan | gen_loss: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████████████████████████████████████▎ | 48/50 [14:35<00:37, 18.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 47 | disc_loss: nan | gen_loss: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|██████████████████████████████████████████▏| 49/50 [14:51<00:18, 18.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 48 | disc_loss: nan | gen_loss: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 50/50 [15:08<00:00, 18.18s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 49 | disc_loss: nan | gen_loss: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.random import uniform\n",
    "from tensorflow.dtypes import float32\n",
    "\n",
    "# Random noise for sampling both generators\n",
    "noise = uniform([sample_size, noise_dim], dtype=float32)\n",
    "\n",
    "print('WGAN_GP without GS version train')\n",
    "no_gs_wgan = NoGS_WGAN_GP(gan_args, n_discriminator)\n",
    "no_gs_wgan.fit(data, train_args, num_cols, cat_cols)\n",
    "\n",
    "print('WGAN_GP with GS version train')\n",
    "gs_wgan = WGAN_GP(gan_args, n_discriminator)\n",
    "gs_wgan.fit(data, train_args, num_cols, cat_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample both generators - We use raw samples (before inverting the data preprocessing) in order to compare the samples as they are returned by the generator\n",
    "no_gs_samples = no_gs_wgan.generator(noise, training=False).numpy()\n",
    "gs_samples = gs_wgan.generator(noise, training=False).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "# Get the input/output data preprocessor map to help us isolate the categorical feats output\n",
    "preprocessor_map = gs_wgan.processor.col_transform_info\n",
    "\n",
    "# Isolate the categorical features and get the feature names\n",
    "n_num_feats = len(preprocessor_map.numerical.feat_names_in)\n",
    "cat_out_names = preprocessor_map.categorical.feat_names_out\n",
    "\n",
    "# Place the categorical parts of the samples in Pandas DataFrames\n",
    "no_gs_cat_raw = DataFrame(no_gs_samples[:,n_num_feats:], columns=cat_out_names)\n",
    "gs_cat_raw = DataFrame(gs_samples[:,n_num_feats:], columns=cat_out_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>workclass_7</th>\n",
       "      <th>workclass_8</th>\n",
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       "      <th>...</th>\n",
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       "      <th>4</th>\n",
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       "   workclass_0  workclass_1  workclass_2  workclass_3  workclass_4  \\\n",
       "0    -0.001279     0.001676     0.023038     0.014748    -0.006284   \n",
       "1     0.010240     0.016984     0.015120    -0.004757     0.978039   \n",
       "2    -0.016967    -0.008452     0.012696    -0.000721     0.986774   \n",
       "3     0.009937    -0.020584     0.043942     0.013131     0.998781   \n",
       "4     0.003388    -0.007593     0.011361     0.002632     0.692322   \n",
       "\n",
       "   workclass_5  workclass_6  workclass_7  workclass_8  education_0  ...  \\\n",
       "0     0.016614     1.018272    -0.026452    -0.017499     0.946260  ...   \n",
       "1    -0.008948     0.002473    -0.021663    -0.026830     0.000806  ...   \n",
       "2     0.014976    -0.008874    -0.033584    -0.012242    -0.020611  ...   \n",
       "3     0.023007    -0.016201     0.010251    -0.054098    -0.010206  ...   \n",
       "4     0.278880     0.016136    -0.029113    -0.032450    -0.013865  ...   \n",
       "\n",
       "   native-country_34  native-country_35  native-country_36  native-country_37  \\\n",
       "0           0.000024          -0.014488          -0.000363           0.006452   \n",
       "1          -0.012645           0.000696          -0.014146           0.020486   \n",
       "2          -0.002892           0.002459           0.001835          -0.004611   \n",
       "3          -0.003774           0.003178           0.018285           0.014908   \n",
       "4           0.007831          -0.015117          -0.020645           0.005496   \n",
       "\n",
       "   native-country_38  native-country_39  native-country_40  native-country_41  \\\n",
       "0           0.010287           1.013489           0.008180           0.012834   \n",
       "1           0.023706           0.972446          -0.011433           0.015508   \n",
       "2           0.009137           1.007913           0.007549           0.007594   \n",
       "3           0.020729           0.999417          -0.032669           0.044523   \n",
       "4           0.018647           1.028197          -0.003226          -0.000077   \n",
       "\n",
       "   target_0  target_1  \n",
       "0  0.020335  0.986271  \n",
       "1  0.993865  0.014315  \n",
       "2  1.038260 -0.002631  \n",
       "3 -0.020730  0.984257  \n",
       "4 -0.002309  1.007212  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the categorical outputs of the generator without GS\n",
    "no_gs_cat_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass_0  workclass_1  workclass_2  workclass_3  workclass_4  \\\n",
       "0          0.0          1.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          1.0          0.0   \n",
       "3          1.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   workclass_5  workclass_6  workclass_7  workclass_8  education_0  ...  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0  ...   \n",
       "1          1.0          0.0          0.0          0.0          1.0  ...   \n",
       "2          0.0          0.0          0.0          0.0          0.0  ...   \n",
       "3          0.0          0.0          0.0          0.0          0.0  ...   \n",
       "4          0.0          0.0          1.0          0.0          0.0  ...   \n",
       "\n",
       "   native-country_34  native-country_35  native-country_36  native-country_37  \\\n",
       "0                0.0                0.0                0.0                0.0   \n",
       "1                0.0                0.0                0.0                0.0   \n",
       "2                0.0                0.0                0.0                0.0   \n",
       "3                0.0                0.0                0.0                0.0   \n",
       "4                0.0                0.0                0.0                0.0   \n",
       "\n",
       "   native-country_38  native-country_39  native-country_40  native-country_41  \\\n",
       "0                0.0                0.0                0.0                0.0   \n",
       "1                0.0                0.0                0.0                0.0   \n",
       "2                0.0                0.0                0.0                0.0   \n",
       "3                0.0                0.0                0.0                0.0   \n",
       "4                0.0                0.0                0.0                1.0   \n",
       "\n",
       "   target_0  target_1  \n",
       "0       0.0       1.0  \n",
       "1       0.0       1.0  \n",
       "2       1.0       0.0  \n",
       "3       0.0       1.0  \n",
       "4       1.0       0.0  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the categorical outputs of the generator without\n",
    "gs_cat_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthesized samples categorical distribution\n",
    "In this comparison we are looking at histograms of the categorical distributions of the synthetic samples from the real data, the GS WGAN_GP and the WGAN_GP without GS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Synthetic data generation: 100%|██████████| 301/301 [00:00<00:00, 1087.94it/s]\n",
      "Synthetic data generation: 100%|██████████| 301/301 [00:02<00:00, 142.63it/s]\n"
     ]
    }
   ],
   "source": [
    "# Sample the real dataset and the generators\n",
    "real_samples = data.sample(n=sample_size)\n",
    "no_gs_samples = no_gs_wgan.sample(sample_size)[:sample_size]\n",
    "gs_samples = gs_wgan.sample(sample_size)[:sample_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "from ydata_synthetic.utils.misc.colormaps import ydata_colormap\n",
    "\n",
    "ydata_colors = ydata_colormap(1)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=len(cat_cols), ncols=3, figsize= (20,20))\n",
    "for i, feat in enumerate(cat_cols):\n",
    "    xticks=sorted(list(data[feat].unique()))\n",
    "    for j, (name, samples) in enumerate({'Real samples': real_samples, 'WGAN_GP with GS': gs_samples, 'WGAN_GP without GS': no_gs_samples}.items()):\n",
    "        (samples[feat].value_counts()/(0.01*sample_size)).plot(kind='bar', ax=axes[i, j], title=(name +'\\n' if i==0 else '') + feat, colormap=ydata_colors, edgecolor='black',\n",
    "        xticks=xticks, rot = 0 if len(xticks)<20 else None)\n",
    "        axes[i, j].yaxis.set_major_formatter(PercentFormatter())\n",
    "fig.tight_layout(pad=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did you notice that the WGAN_GP without Gumbel-Softmax has a behaviour close to winner takes all in most of the features?\n",
    "On the other hand the Gumbel Softmax lies in the balance between an uniform distribution and the winner takes all behaviour.\n",
    "This is an expected trade-off that can be calibrated with the temperature argument of the Gumbel-Softmax (tau). You can learn more about this behaviour in the foundational articles shared in the introductory part.\n",
    "\n",
    "With this final demonstration we are concluding the notebook, did enjoy learning about Gumbel-Softmax?\n",
    "By default this feature is implemented in all the regular generators.\n",
    "\n",
    "Feel free to adapt this notebook to play with different parameters, datasets or even other GANs of the repository.\n",
    "\n",
    "Also, enjoy the improved categorical generation!"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "98ab1d40448dd008ad1b7decdd6b8b1a271c78de377aef983939bf1a8fd37a83"
  },
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
